PRESTRESSED I-GIRDER OPTIMIZATION USING GENETIC ALGORITHM

Genetic Algorithm (GA) is a method of optimization mimicking the very process of evolution and natural selection. The results can be seen in nature, as all <br /> <br /> <br /> creatures are optimized to fit their environments. Proves to work rather well, it suggests that this me...

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Main Author: ADIBASKORO (NIM : 15010086), TITO
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/24549
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:24549
spelling id-itb.:245492017-09-27T10:25:49ZPRESTRESSED I-GIRDER OPTIMIZATION USING GENETIC ALGORITHM ADIBASKORO (NIM : 15010086), TITO Indonesia Final Project INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/24549 Genetic Algorithm (GA) is a method of optimization mimicking the very process of evolution and natural selection. The results can be seen in nature, as all <br /> <br /> <br /> creatures are optimized to fit their environments. Proves to work rather well, it suggests that this method can be adopted to optimize engineering problems. With <br /> <br /> <br /> the right set up and modeling of individuals and their environment, Genetic Algorithm can optimize pretty much any kind of problems, including - in this case - prestressed I-girder. With it usually being costly, optimization will save considerable amount of budget as well as resource. With numerical approach on many cases, and using AASHTO LRFD 2007 as code and constraints, thorough analysis is done, including ultimate strength, service stresses and deflection, detailing, geometrical feasibility, etc. One of the results of the present study is a GA based optimization software with the suitable approach in order to carry out the process of optimizing prestressed I-girders, including the proving of the effectiveness of the newly applied improvements. By using this software, the other result of this study is to show the best result as well as the other optimum <br /> <br /> <br /> solutions, since running it multiple times will give multiple yet closely optimized selections of results. Lastly, sensitivity analysis is carried out to check the <br /> <br /> <br /> uncertainty level of the software performance regarding the input variation. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description Genetic Algorithm (GA) is a method of optimization mimicking the very process of evolution and natural selection. The results can be seen in nature, as all <br /> <br /> <br /> creatures are optimized to fit their environments. Proves to work rather well, it suggests that this method can be adopted to optimize engineering problems. With <br /> <br /> <br /> the right set up and modeling of individuals and their environment, Genetic Algorithm can optimize pretty much any kind of problems, including - in this case - prestressed I-girder. With it usually being costly, optimization will save considerable amount of budget as well as resource. With numerical approach on many cases, and using AASHTO LRFD 2007 as code and constraints, thorough analysis is done, including ultimate strength, service stresses and deflection, detailing, geometrical feasibility, etc. One of the results of the present study is a GA based optimization software with the suitable approach in order to carry out the process of optimizing prestressed I-girders, including the proving of the effectiveness of the newly applied improvements. By using this software, the other result of this study is to show the best result as well as the other optimum <br /> <br /> <br /> solutions, since running it multiple times will give multiple yet closely optimized selections of results. Lastly, sensitivity analysis is carried out to check the <br /> <br /> <br /> uncertainty level of the software performance regarding the input variation.
format Final Project
author ADIBASKORO (NIM : 15010086), TITO
spellingShingle ADIBASKORO (NIM : 15010086), TITO
PRESTRESSED I-GIRDER OPTIMIZATION USING GENETIC ALGORITHM
author_facet ADIBASKORO (NIM : 15010086), TITO
author_sort ADIBASKORO (NIM : 15010086), TITO
title PRESTRESSED I-GIRDER OPTIMIZATION USING GENETIC ALGORITHM
title_short PRESTRESSED I-GIRDER OPTIMIZATION USING GENETIC ALGORITHM
title_full PRESTRESSED I-GIRDER OPTIMIZATION USING GENETIC ALGORITHM
title_fullStr PRESTRESSED I-GIRDER OPTIMIZATION USING GENETIC ALGORITHM
title_full_unstemmed PRESTRESSED I-GIRDER OPTIMIZATION USING GENETIC ALGORITHM
title_sort prestressed i-girder optimization using genetic algorithm
url https://digilib.itb.ac.id/gdl/view/24549
_version_ 1821844706496086016